Calculating the power of a planned individual participant data meta-analysis to examine prognostic factor effects for a binary outcome.
individual participant data (IPD) meta‐analysis
power
predictor effects
prognostic factor effects
sample size
Journal
Research synthesis methods
ISSN: 1759-2887
Titre abrégé: Res Synth Methods
Pays: England
ID NLM: 101543738
Informations de publication
Date de publication:
24 Jul 2024
24 Jul 2024
Historique:
revised:
09
05
2024
received:
17
01
2024
accepted:
26
06
2024
medline:
24
7
2024
pubmed:
24
7
2024
entrez:
24
7
2024
Statut:
aheadofprint
Résumé
Collecting data for an individual participant data meta-analysis (IPDMA) project can be time consuming and resource intensive and could still have insufficient power to answer the question of interest. Therefore, researchers should consider the power of their planned IPDMA before collecting IPD. Here we propose a method to estimate the power of a planned IPDMA project aiming to synthesise multiple cohort studies to investigate the (unadjusted or adjusted) effects of potential prognostic factors for a binary outcome. We consider both binary and continuous factors and provide a three-step approach to estimating the power in advance of collecting IPD, under an assumption of the true prognostic effect of each factor of interest. The first step uses routinely available (published) aggregate data for each study to approximate Fisher's information matrix and thereby estimate the anticipated variance of the unadjusted prognostic factor effect in each study. These variances are then used in step 2 to estimate the anticipated variance of the summary prognostic effect from the IPDMA. Finally, step 3 uses this variance to estimate the corresponding IPDMA power, based on a two-sided Wald test and the assumed true effect. Extensions are provided to adjust the power calculation for the presence of additional covariates correlated with the prognostic factor of interest (by using a variance inflation factor) and to allow for between-study heterogeneity in prognostic effects. An example is provided for illustration, and Stata code is supplied to enable researchers to implement the method.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Engineering & Physical Sciences Research Council (EPSRC)
ID : EP/Y018516/1
Organisme : Medical Research Council - National Institute for Health and Care Research (MRC-NIHR)
ID : MR/V038168/1
Organisme : Cancer Research UK
ID : C49297/A27294
Pays : United Kingdom
Informations de copyright
© 2024 The Author(s). Research Synthesis Methods published by John Wiley & Sons Ltd.
Références
Riley RD, Tierney J, Stewart LA. Individual Participant Data Meta‐Analysis: A Handbook for Healthcare Research. Wiley; 2021.
Ensor J, Burke DL, Snell KIE, Hemming K, Riley RD. Simulation‐based power calculations for planning a two‐stage individual participant data meta‐analysis. BMC Med Res Methodol. 2018;18(1):41.
Riley RD, Hattle M, Collins GS, Whittle R, Ensor J. Calculating the power to examine treatment‐covariate interactions when planning an individual participant data meta‐analysis of randomized trials with a binary outcome. Stat Med. 2022;41:4822‐4837.
Riley RD, Collins GS, Hattle M, Whittle R, Ensor J. Calculating the power of a planned individual participant data meta‐analysis of randomised trials to examine a treatment‐covariate interaction with a time‐to‐event outcome. Res Synth Methods. 2023;14(5):718‐730.
Riley RD, Hayden JA, Steyerberg EW, et al. Prognosis research strategy (PROGRESS) 2: prognostic factor research. PLoS Med. 2013;10(2):e1001380.
Riley RD, van der Windt D, Croft P, Moons KGM. Prognosis Research in Health Care: Concepts, Methods, and Impact. Oxford University Press; 2019.
Simmonds MC, Higgins JP. Covariate heterogeneity in meta‐analysis: criteria for deciding between meta‐regression and individual patient data. Stat Med. 2007;26(15):2982‐2999.
Fisher DJ, Copas AJ, Tierney JF, Parmar MKB. A critical review of methods for the assessment of patient‐level interactions in individual participant data meta‐analysis of randomized trials, and guidance for practitioners. J Clin Epidemiol. 2011;64(9):949‐967.
Thompson S, Kaptoge S, White I, et al. Statistical methods for the time‐to‐event analysis of individual participant data from multiple epidemiological studies. Int J Epidemiol. 2010;39(5):1345‐1359.
Riley RD, Ensor J, Hattle M, Papadimitropoulou K, Morris TP. Two‐stage or not two‐stage? That is the question for IPD meta‐analysis projects. Res Synth Methods. 2023;14(6):903‐910.
Fisher DJ. Two‐stage individual participant data meta‐analysis and generalized forest plots. Stata J. 2015;15(2):369‐396.
Viechtbauer W. Conducting meta‐analyses in R with the metafor package. J Stat Softw. 2010;36(3):1‐48.
Langan D, Higgins JPT, Jackson D, et al. A comparison of heterogeneity variance estimators in simulated random‐effects meta‐analyses. Res Synth Methods. 2019;10(1):83‐98.
Demidenko E. Sample size and optimal design for logistic regression with binary interaction. Stat Med. 2008;27(1):36‐46.
Schmoor C, Sauerbrei W, Schumacher M. Sample size considerations for the evaluation of prognostic factors in survival analysis. Stat Med. 2000;19(4):441‐452.
Whittemore AS. Sample size for logistic regression with small response probability. J Am Stat Assoc. 1981;76(373):27‐32.
Hsieh FY, Bloch DA, Larsen MD. A simple method of sample size calculation for linear and logistic regression. Stat Med. 1998;17(14):1623‐1634.
Poynard T, Calès P, Pasta L, et al. Beta‐adrenergic‐antagonist drugs in the prevention of gastrointestinal bleeding in patients with cirrhosis and esophageal varices. An analysis of data and prognostic factors in 589 patients from four randomized clinical trials. Franco‐Italian Multicenter Study Group. N Engl J Med. 1991;324(22):1532‐1538.
Hartung J, Knapp G. A refined method for the meta‐analysis of controlled clinical trials with binary outcome. Stat Med. 2001;20(24):3875‐3889.
Sidik K, Jonkman JN. A simple confidence interval for meta‐analysis. Stat Med. 2002;21(21):3153‐3159.